The listing or discussion of an apparently prior-published document in this specification should not necessarily be taken as an acknowledgement that the document is part of the state of the art or is common general knowledge.
Statistical models predicting the occurrence of CVD (cardiovascular disease), covering a range of conditions including fatal and non-fatal myocardial infarction, angina, transient ischemia, intermittent claudication and stroke, have been in existence for over thirty years, with the most prominent of these published by investigators from the Framingham Heart Study (Anderson et al., 1991, Wilson et al., 1998). These are used widely by clinicians in order to calculate an individual patient's risk of CVD and stratify patients for risk factor reduction, such as prescribing medication or recommending dietary changes and exercise regimens. The widespread use of such statistical models has been facilitated by their actual as well as perceived validity, as assessed by the capacity of risk scores derived from the models to predict CVD in multiple populations beyond the original study. This has led to risk scores being recommended in a number of international guidelines for cholesterol treatment in particular. The use of these models has also been facilitated by their simplicity, generally requiring input by clinicians of the results of simple tests of blood pressure, cholesterol, diabetic status and self reported smoking behaviour, together with development of simple “tools” designed to simplify the calculation process (using charts, software deployed on CD-ROM, internet or handheld digital devices). In particular, US 2005/0261558 discloses a tool implemented in logic on a computing device such as a PDA (Personal Digital Assistant) that permits a user to input patient-specific data relevant to evaluating risk for CVD and calculating an equivalent age of the patient, based on the Framingham data set and the input data.
Such developments have been of value to doctors and their patients. Further evaluation of methods for communicating risk to clinicians has led to the development of different risk framing methods. For example, an age-matched CVD risk has been demonstrated to increase the likelihood that individuals will perceive a high risk score (as computed by Framingham risk scores) to be high (Fair et al., 2008). Further elaboration on this concept to create a “cardiovascular disease risk adjusted age” (Goldman et al, 2006), or “Heart Age” (Goldman et al, 2006) has also shown to be well understood by patient populations. This is of critical importance since models of health behaviour highlight the importance that an individual needs to have a heightened sense of perceived susceptibility to disease before taking action.
Whilst clinicians have been the foot soldiers in the treatment of disease and prevention in high risk individuals, the global burden of cardiovascular disease is sustained by poor health in entire populations, necessitating a method for raising awareness of CVD risk outside clinical settings, i.e. in the wider population. In order to maximise the potential to reduce risk at a population level, it is important to penetrate the vast majority of the population to try and reduce CVD risk factors. Whilst clinicians are advised to prescribe cholesterol-lowering medication to those at 20% risk of CHD or greater (Adult Treatment Panel III, 2001), Ajani et al (2006) have estimated that just 13.7% of the US population fall into the >20% risk category, using NHANES (National Health and Nutrition Examination Survey) data. Furthermore, those with <10% risk who are deemed “low risk” by clinical standards comprise greater than 75% of the population. If the US population currently stands at more than 300 million, it follows that approximately 40 million people have the potential to have their CVD risk reduced by their clinician. Assuming that patients achieve a reduction in CVD risk of 5% using medication for lowering blood pressure and cholesterol, then the number of estimated primary CVD events in the subsequent ten years in this group will fall from 8 million to 6 million.
On the other hand, it also follows that approximately 225 million people are at <10% risk in the US population yet are much less likely to be targeted by clinicians for risk reduction purposes. Nevertheless up to 22.5 million people are estimated to have a primary CVD event in the subsequent ten years from this group, far in excess of those estimated as likely to have events in the high risk population. Furthermore, to achieve a similar magnitude of risk reduction in this population (i.e. 2 million events) the risk need only be reduced by <1%. The greater potential of this approach is adequately demonstrated by the fact that a risk reduction of 2% could lead to over 4.5 million CVD events being prevented.
An individual user's heart age can be defined as being the chronological age of a population that is at a low or normal risk of cardiovascular disease for their age, and whose risk of CVD is closest or equal to that of the individual user. The heart age is consequently the age at which an individual's measured cardiovascular risk would be defined as “normal” according to international guidelines.
A major challenge exists in estimating heart age outside of clinical settings, given the measurements that are generally required to calculate a valid estimate. For example, serum total cholesterol and HDL (high-density lipoprotein) cholesterol require a blood sample to be taken, which reduces the convenience to users and increases costs. Therefore new methods are required for optimising the process of estimating heart age according to the measures that may be available. These estimates should not, however, be generated at the expense of other more accurate CVD risk estimates, nor should such estimates fail to identify those who may require further blood tests for determining a possible high risk status (e.g. diabetes or hypercholesterolemia). Often such decisions are a question of cost and so the capability to alter the thresholds for these finding such “cases” should be adaptable based on the resource needs of a particular country.
Ajani et al estimated from the NHANES survey that 60.8% of those with 10% risk and 74.1% of those with 10-20% risk are overweight in the US population. Critically, overweight status is associated with 1) increased prevalence of CVD risk factors (blood pressure, diabetes, high total cholesterol, low HDL cholesterol) included in the Framingham Risk Score and 2) increased risk of incident CVD risk factors included in the Framingham risk score or changes in CVD risk factors over time. Therefore targeting a “heart health” message to populations at <20% risk leading to a change in health behaviours consistent with a risk factor reduction would have substantial public health benefits.
Finally, whilst cholesterol is inconvenient to measure on a large scale messages about cholesterol lowering should still be promoted to individuals within a population. Therefore, it is important that any method should have the capability to estimate a range of cholesterol values to which an individual can be assigned to if that individual chooses not to or is unable to take a blood test.
It is an object of the invention to address one or more of the above mentioned problems.